Canadian AgTech · AI · Robotics

Precision agriculture.
Without the chemicals.

CanTechPlus combines artificial intelligence, computer vision and precision laser technology to identify and treat unwanted weeds with extraordinary accuracy — while protecting crops, soil and surrounding ecosystems.

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CanTechPlus autonomous agricultural robot with multi-camera sensor tower and precision laser module

The opportunity

Modernfarmingdeservesamoreprecisesolution.

Conventional blanket treatment applies the same input to the entire area — the crop, the soil between rows and everything living around it. Precision technology makes a narrower, more deliberate approach possible.

Young crop seedlings growing in dark soil with broadleaf weeds between the rows

Field reality

Crops and weeds share the same row, the same light and the same soil.

Traditional approach

Treat the entire area

Everything inside the boundary receives treatment, whether it needs it or not.

CanTechPlus approach

Identify. Target. Treat only what is necessary.

Only recognised targets are treated. Surrounding vegetation is left untouched.

SoilWaterBiodiversityBeneficial organismsLong-term ecosystem health

How it works

Seetheweed.Targettheweed.Protecteverythingelse.

Four stages run in a continuous loop, many times per second, as the platform moves through the field.

  1. 01

    See

    Computer vision scans the agricultural environment continuously as the platform moves through the field.

  2. 02

    Understand

    Machine learning distinguishes crops, weeds and other agricultural targets frame by frame.

  3. 03

    Target

    Precision targeting calculates the correct treatment point for each recognised target.

  4. 04

    Treat

    Precision laser energy is delivered to the selected target while surrounding vegetation remains untouched.

Camera and sensor capture
Stage 01 / 04

The platform

Agriculture.
Precisely redefined.

01 / 05

Vision

A multi-camera sensor tower observes the row ahead, capturing the scene the models reason about.

CanTechPlus agricultural robot, three-quarter view

Core technology

Anengineeringstackbuiltaroundonedecision:treat,ordon’t.

AI Model Training

Custom machine-learning models trained around agricultural recognition challenges.

Computer Vision

Real-time visual identification of crops, weeds and relevant agricultural targets.

Object Detection

Selective recognition designed to distinguish wanted vegetation from unwanted targets.

Edge Intelligence

Fast processing close to the agricultural environment.

Precision Laser Control

Focused treatment of specific targets instead of blanket application.

Multi-Sensor Fusion

Combining visual, depth, thermal or other sensor inputs when supported by the system.

System architecture

Insidetheintelligenceloop.

Each module hands a more specific decision to the next, until only a single target coordinate remains.

  1. 01

    Field

    Crop rows, soil, light

  2. 02

    Sensors

    Cameras, depth, environment

  3. 03

    AI

    On-board inference

  4. 04

    Recognition

    Crop vs. weed

  5. 05

    Targeting

    Treatment coordinate

  6. 06

    Treatment

    Precision laser

Target accuracy rate

0.0%

The target accuracy rate reported for the CanTechPlus recognition and targeting system.

Chemical contamination

0%

Chemical-free targeting: treatment is delivered as focused laser energy, not as a chemical application.

Approach

Selective
by design

Specifications and performance may vary depending on application, crop and deployment conditions.

Precision comparison

Treatthetarget.Nottheentirefield.

Drag to compare a blanket treatment boundary with AI-guided selective targeting.

Rows of leafy green crops growing in an open field
⇔

AI vision

Whatthemachinesees.

Toggle the perception layer to view classification, confidence and target coordinates on a real crop scene.

Crop seedlings and weeds growing in soil, used to demonstrate AI detection

Illustrative visualisation of the perception concept. Detection output varies with crop, weed species, lighting and field conditions.

Sensing & intelligence

It doesn’t just look.
It understands.

Close-up of a multi-camera sensor module with a LiDAR unit mounted on top
  • 01

    Visual sensing

  • 02

    Depth perception

  • 03

    Environment analysis

  • 04

    Object recognition

  • 05

    AI processing

  • 06

    Target calculation

Sustainability

Betterprecisioncanmeanalighterfootprint.

Technology that works with nature: protect agricultural productivity while reducing the environmental burden associated with conventional pest-management methods.

A hand holding a clump of healthy dark soil with fine roots

  • Water Protection

    Reducing reliance on chemical treatments helps limit chemical runoff.

  • Soil Preservation

    Precision treatment supports strategies designed to protect soil ecosystems.

  • Selective Treatment

    Only identified targets are treated.

  • Reduced Chemical Dependency

    Technology designed around physical precision rather than blanket application.

Precision over saturation — a measured way to think about crop protection.

Sustainability
Water droplets resting on a green crop leaf at sunrise

Field applications

Whereprecisionearnsitsplace.

Focus areas and research directions for the CanTechPlus platform. Suitability is assessed per crop, site and deployment.

Aerial view of Canadian farmland with a patchwork of crop fields
Primary focus

Crop fields

Open-field row crops where weeds and crops share the same ground.

Interior of a modern greenhouse with rows of leafy greens
Potential application

Greenhouses & controlled agriculture

Structured, repeatable environments for perception and targeting work.

Rows of lettuce growing under shade structures
Potential application

Vegetable & specialty crops

High-value crops where selective treatment matters most.

Weeds growing between young crop seedlings
Primary focus

Weed management

Recognition and treatment of unwanted vegetation between crop rows.

Engineers calibrating a camera and LiDAR sensor module in a robotics lab
Research direction

Research environments

Collaboration on datasets, model performance and field trials.

FAQ

Clearanswers.